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Local Differential Privacy in the Medical Domain to Protect Sensitive Information: Algorithm Development and Real-World Validation

 MinDong Sung  ;  Dongchul Cha  ;  Yu Rang Park 
 JMIR MEDICAL INFORMATICS, Vol.9(11) : e26914, 2021-11 
Journal Title
Issue Date
algorithm ; big data ; development ; differential privacy ; electronic health record ; feasibility ; machine learning ; medical data ; medical informatics ; privacy ; privacy-preserving ; synthetic data ; validation
Background: Privacy is of increasing interest in the present big data era, particularly the privacy of medical data. Specifically, differential privacy has emerged as the standard method for preservation of privacy during data analysis and publishing.

Objective: Using machine learning techniques, we applied differential privacy to medical data with diverse parameters and checked the feasibility of our algorithms with synthetic data as well as the balance between data privacy and utility.

Methods: All data were normalized to a range between -1 and 1, and the bounded Laplacian method was applied to prevent the generation of out-of-bound values after applying the differential privacy algorithm. To preserve the cardinality of the categorical variables, we performed postprocessing via discretization. The algorithm was evaluated using both synthetic and real-world data (from the eICU Collaborative Research Database). We evaluated the difference between the original data and the perturbated data using misclassification rates and the mean squared error for categorical data and continuous data, respectively. Further, we compared the performance of classification models that predict in-hospital mortality using real-world data.

Results: The misclassification rate of categorical variables ranged between 0.49 and 0.85 when the value of ε was 0.1, and it converged to 0 as ε increased. When ε was between 102 and 103, the misclassification rate rapidly dropped to 0. Similarly, the mean squared error of the continuous variables decreased as ε increased. The performance of the model developed from perturbed data converged to that of the model developed from original data as ε increased. In particular, the accuracy of a random forest model developed from the original data was 0.801, and this value ranged from 0.757 to 0.81 when ε was 10-1 and 104, respectively.

Conclusions: We applied local differential privacy to medical domain data, which are diverse and high dimensional. Higher noise may offer enhanced privacy, but it simultaneously hinders utility. We should choose an appropriate degree of noise for data perturbation to balance privacy and utility depending on specific situations.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Otorhinolaryngology (이비인후과학교실) > 1. Journal Papers
Yonsei Authors
Park, Yu Rang(박유랑) ORCID logo https://orcid.org/0000-0002-4210-2094
Sung, MinDong(성민동) ORCID logo https://orcid.org/0000-0002-5217-8877
Cha, Dongchul(차동철) ORCID logo https://orcid.org/0000-0002-0043-5026
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